Cargando…
A study on skin tumor classification based on dense convolutional networks with fused metadata
Skin cancer is the most common cause of death in humans. Statistics show that competent dermatologists have a diagnostic accuracy rate of less than 80%, while inexperienced dermatologists have a diagnostic accuracy rate of less than 60%. The higher rate of misdiagnosis will cause many patients to mi...
Autores principales: | Yin, Wenjun, Huang, Jianhua, Chen, Jianlin, Ji, Yuanfa |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9806866/ https://www.ncbi.nlm.nih.gov/pubmed/36601473 http://dx.doi.org/10.3389/fonc.2022.989894 |
Ejemplares similares
-
A multimodal transformer to fuse images and metadata for skin disease classification
por: Cai, Gan, et al.
Publicado: (2022) -
Skin Lesion Classification Using Densely Connected Convolutional Networks with Attention Residual Learning
por: Wu, Jing, et al.
Publicado: (2020) -
Densely Convolutional Spatial Attention Network for nuclei segmentation of histological images for computational pathology
por: Islam Sumon, Rashadul, et al.
Publicado: (2023) -
Adversarial dense graph convolutional networks for single-cell classification
por: Wang, Kangwei, et al.
Publicado: (2023) -
Lightweight 3D Dense Autoencoder Network for Hyperspectral Remote Sensing Image Classification
por: Bai, Yang, et al.
Publicado: (2023)